In this paper, a novel hybrid Invasive Weed Optimization (IWO) and Cuckoo Search (CS) algorithm (IWO/CS) is presented for phase-only pattern synthesis of large array antenna. The IWO/CS embeds the Levy flight CS into IWO as global guide, thereby enhancing search ability algorithm. At same time, in order to avoid trapping local optimal solution, mutation operator introduced enhance Finally, comp...

The performance of compressed sensing (CS) algorithms is dependent on the sparsity level of the underlying signal, the type of sampling pattern used and the reconstruction method applied. The higher the incoherence of the sampling pattern used for undersampling, less aliasing will be noticeable in the aliased signal space, resulting in better CS reconstruction. In this work, based on point spre...

Compressed sensing (CS) is a novel method to measure and reconstruct N-dimensional compressible signals from M << N linear-combination (e.g. Fourier-component) samples [1,2]. CS has been applied to brain MRI to achieve acceleration factors R = N/M of 2–3 with only modest degradation in image quality [3]. A further reduction in the scan time can be achieved by multi-coil parallel MRI (pMRI) [4]....

Compressive Sensing (CS) theory breaks through the limitations of traditional Nyquist sampling theorem, accomplishes the compressive sampling and reconstruction of signals based on sparsity or compressibility. In this paper CS is presented in a Bayesian framework for linear frequency modulated (LFM) cases whose likelihood or priors are usually Gaussian. In order to decrease the sampling pressur...

Background Sampling bias, like survey participants' nonresponse, needs to be adequately addressed in the analysis of sampling designs. Often survey weights will be calibrated on specific covariates related to the probability of selection and nonresponse to get representative population estimates. However, such calibrated survey (CS) weights are usually constructed for cross-sectional results, b...

The emergence of compressed sensing (CS) theory provides potential hardware architecture to sub-Nyquist sample the wideband signals. However, applying this discrete CS model to continuous analogue signals is not an easy task. The modulated wideband converter (MWC) is an efficient wideband compressed sampling architecture for the sparse multiband signals. In this paper, a soft-calibration system...

W HILE it is common knowledge that most images can be greatly compressed, compressive sensing (CS) theory has established that such compression can be done during the data acquisition process and then the uncompressed image can be recovered through a computationally tractable optimization procedure such as L1-norm minimization [1]–[4], or greedy algorithms [5]. In the field of biomedical imagin...

ompressive sensing (CS) is a new method for image sampling in contrast with well-known Nyquist sampling theorem. In addition to the sampling and sparse domain which play an important role in perfect signal recovery on CS framework, the recovery algorithm which has been used also has effects on the reconstructed image. In this paper, the performance of four recovery algorithms are compared accor...

This paper describes the development of a sub-Nyquist sampling system that can digitize high-speed signals using a low-speed analog to digital converter (ADC). The system is implemented by a field programmable gate array (FPGA), and it is possible to make change to the equivalent sampling frequency according to the practical applications. As an application of the compressed sensing (CS) theory,...